We propose a few-shot learning method for feature selection that can select relevant
features given a small number of labeled instances. Existing methods require many labeled …

Importance weighting is a fundamental procedure in statistics and machine learning that
weights the objective function or probability distribution based on the importance of the …

We propose a method to learn prediction models such as classifiers for unseen target tasks
where labeled and unlabeled data are absent but a few relevant input features for solving …

In this paper, the authors propose a new approach for determining the mixture proportions of
positive and negative instances in both a small set of positively labeled instances …

We propose a meta-learning method to improve the anomaly detection performance on
unseen target tasks that have only unlabeled data. Existing meta-learning methods for …

We propose a meta-learning method for positive and unlabeled (PU) classification, which
improves the performance of binary classifiers obtained from only PU data in unseen target …

This paper proposes a model estimation method in offline Bayesian model-based
reinforcement learning (MBRL). Learning a Bayes-adaptive Markov decision process …

This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the
integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio …

In the present paper we introduce new optimization algorithms for the task of density ratio
estimation. More precisely, we consider extending the well-known KMM method using the …

Density ratio estimation has a broad application in the world of machine learning and data
science, especially in transfer learning and contrastive learning. This work mainly focuses …